Application of cyclostationarity analysis to image forensics

The term cyclostationarity refers to a special class of signals which exhibit periodicity in their statistics. In this work, we focused on geometrical transformations and showed that images that have undergone such transformations contain hidden cyclostationary features. This justifies employing the well–developed theory of cyclostationarity and its efficient methods for analyzing images' history in respect to geometrical transformations.

Theory of cyclostationarity has shown that a cyclostationary signal has a frequency spectrum that is correlated with a shifted version of itself . Based on this, we focused on detecting the traces of cyclostationarity by estimating the spectral correlation function. The picture below shows some example of the method's output. Distinctive peaks signify the presence of cyclostationary features in shown images.

Results obtained are promising and show that employing cyclostationarity methods can be valuable in image forensics. Further research might explore application and evaluation of various types of cyclostationary feature detection methods, use of filter banks and extension to other geometric transformations.